332 citations · 1.5k across the 59 of their papers we have counts for
11 papers · 1 filter
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
Qing Ma, Junjun Jiang, Xianming Liu +1
To solve the ill-posed problem of hyperspectral image super-resolution (HSISR), an usually method is to use the prior information of the hyperspectral images (HSIs) as a regulariza…
A Practical Contrastive Learning Framework for Single-Image Super-Resolution
Gang Wu, Junjun Jiang, Xianming Liu
Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challen…
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network
Yuanzhi Wang, Tao Lu, Yanduo Zhang +4
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps, faci…
Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting
Changan Wang, Qingyu Song, Boshen Zhang +7
Recently, the problem of inaccurate learning targets in crowd counting draws increasing attention. Inspired by a few pioneering work, we solve this problem by trying to predict the…
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
Junjun Jiang, Chenyang Wang, Xianming Liu +2
High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions, such as low-light environment and spoofin…
SDGMNet: Statistic-based Dynamic Gradient Modulation for Local Descriptor Learning
Jiayi Ma, Yuxin Deng
Modifications on triplet loss that rescale the back-propagated gradients of special pairs have made significant progress on local descriptor learning. However, current gradient mod…
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
Pengwei Liang, Junjun Jiang, Xianming Liu +1
The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kern…
Pan-sharpening via High-pass Modification Convolutional Neural Network
Jiaming Wang, Zhenfeng Shao, Xiao Huang +3
Most existing deep learning-based pan-sharpening methods have several widely recognized issues, such as spectral distortion and insufficient spatial texture enhancement, we propose…
Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework
Fu Yuanbin, Guoxiaojie, Hu Qiming +3
The importance of hierarchical image organization has been witnessed by a wide spectrum of applications in computer vision and graphics. Different from image segmentation with the…
Omniscient Video Super-Resolution
Peng Yi, Zhongyuan Wang, Kui Jiang +4
Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previousl…
Deep Learning-based Face Super-Resolution: A Survey
Junjun Jiang, Chenyang Wang, Xianming Liu +1
Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing the resolution of low-resolution (LR) face images to generate high-resolution (HR) face i…